A new graph-theoretic approach to determine the similarity of genome sequences based on nucleotide triplets.

Methods of finding sequence similarity play a significant role in computational biology. Owing to the rapid increase of genome sequences in public databases, the evolutionary relationship of species becomes more challenging. But traditional alignment-based methods are found inappropriate due to their time-consuming nature.

Genome sequence comparison under a new form of tri-nucleotide representation based on bio-chemical properties of nucleotides.

Genetic sequence analysis, classification of genome sequence and evolutionary relationship between species using their biological sequences, are the emerging research domain in Bioinformatics. Several methods have already been applied to DNA sequence comparison under tri-nucleotide representation. In this paper, a new form of tri-nucleotide representation is proposed for sequence comparison.

Optimal choice of k-mer in composition vector method for genome sequence comparison.

Several proteins and genes are members of families that share a public evolutionary. In order to outline the evolutionary relationships and to recognize conserved patterns, sequence comparison becomes an emerging process. The current work investigates critically the k-mer role in composition vector method for comparing genome sequences.

Analyzing text recognition from tactually evoked EEG.

Tactual exploration of objects produce specific patterns in the human brain and hence objects can be recognized by analyzing brain signals during tactile exploration. The present work aims at analyzing EEG signals online for recognition of embossed texts by tactual exploration. EEG signals are acquired from the parietal region over the somatosensory cortex of blindfolded healthy subjects while they tactually explored embossed texts, including symbols, numbers, and alphabets.

A Generic Transferable EEG Decoder for Online Detection of Error Potential in Target Selection.

Reliable detection of error from electroencephalography (EEG) signals as feedback while performing a discrete target selection task across sessions and subjects has a huge scope in real-time rehabilitative application of Brain-computer Interfacing (BCI). Error Related Potentials (ErrP) are EEG signals which occur when the participant observes an erroneous feedback from the system. ErrP holds significance in such closed-loop system, as BCI is prone to error and we need an effective method of systematic error detection as feedback for correction.